Plot film density digitized with WebPlotDigitizer

density_plot.py

#!/usr/bin/env python3

'''
Converts a CSV file exported by WebPlotDigitizer ( https://apps.automeris.io/wpd/ ) to a dcamprof camera SSF JSON
For the time being, the column headers need fixing - Replace the empty entry to the right of Blue in the first row with Blue, etc.
'''
import argparse
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import json
import os

ap = argparse.ArgumentParser()
ap.add_argument('-i', '--input', type=argparse.FileType('rb'), required=True,
    help='path to input files')

args = vars(ap.parse_args())

pd.options.mode.copy_on_write = True

density_data = pd.read_csv(args['input'], header=[0,1])

'''
WebPlotDigitizer exports in the order you added points, and has individual X values for each dataset instead of a single X column that is suitable for indexing,
so let's do some re-sorting.
'''
datasets = {}
for color in ['Red', 'Green', 'Blue']:
    cdata = density_data[color]
    cdata.rename(columns={'Y' : color}, inplace=True)
    cdata.set_index('X', inplace=True, drop=True)
    cdata = cdata[cdata.index.notnull()]
    datasets[color] = cdata

density_data = pd.concat(datasets.values()).sort_index()

# https://stackoverflow.com/questions/35855772/pandas-merge-duplicate-index-into-single-index
density_data = density_data.groupby(density_data.index).sum(min_count=1)

# https://stackoverflow.com/questions/41493282/in-python-pandas-how-can-i-re-sample-and-interpolate-a-dataframe
dstep = 0.1
exposure_vals = np.arange(-4.0,1.0 + dstep, dstep)
density_data = density_data.reindex(density_data.index.union(exposure_vals))

# https://stackoverflow.com/questions/68548971/linearly-extrapolate-pandas-dataframe-using-built-in-interpolate-method
density_data.interpolate(method='slinear', fill_value="extrapolate", limit_direction="both", inplace=True)

density_data = density_data.loc[exposure_vals]

density_data['Red'] -= density_data['Red'].min()
density_data['Green'] -= density_data['Green'].min()
density_data['Blue'] -= density_data['Blue'].min()

plt.plot(density_data['Red'], color='r')
plt.plot(density_data['Green'], color='g')
plt.plot(density_data['Blue'], color='b')

plt.figure()
plt.plot(np.power(10.0,-density_data['Red']), color='r')
plt.plot(np.power(10.0,-density_data['Green']), color='g')
plt.plot(np.power(10.0,-density_data['Blue']), color='b')

plt.figure()
plt.plot(np.power(10,density_data.index),np.power(10.0,-density_data['Red']), color='r')
plt.plot(np.power(10,density_data.index),np.power(10.0,-density_data['Green']), color='g')
plt.plot(np.power(10,density_data.index),np.power(10.0,-density_data['Blue']), color='b')

plt.figure()
plt.semilogx(np.power(10.0,-density_data['Red']), np.log2(np.power(10,density_data.index)), color='r', label='Film Red')
plt.semilogx(np.power(10.0,-density_data['Green']), np.log2(np.power(10,density_data.index)), color='g', label='Film Green')
plt.semilogx(np.power(10.0,-density_data['Blue']), np.log2(np.power(10,density_data.index)), color='b', label='Film Blue')

density_vals = np.linspace(0,2.5,2000)
tcoeff_vals = np.power(10,-density_vals)
plt.semilogx(tcoeff_vals, np.log2(np.power(tcoeff_vals, -1.5)/np.power(10,2.8)), label='Simple Exponent (exp = -1.5)')
plt.xlabel('Normalized Transmission Coefficient (Orange Mask = 1.0)')
plt.ylabel('Scene Light (EV)')
plt.title('Scene Light vs. Film Transmission Coefficient for Fuji Superia Xtra 400')
plt.legend()
plt.show()